The room mesh is first passed as input to a semantic segmantation model, e.g. MinkowskiNet[1]}[2]}. Then we exploit the output predicted labels and ground truth labels respectively in our framework. The semantic segmantation results and final stylized results are displayed in Fig. REF . We can see that the results with ground truth semantics and results with semantic prediction are generally the same, despite some regions with wrongly predicted labels (e.g., the wall between two refrigerators).
As shown in Table. REF , 71.43% of user ratings prefer results generated with ground truth labels than estimated labels.
